wehub-resource-sync
253 lines
8.9 KiB
Python
253 lines
8.9 KiB
Python
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import unittest
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import numpy as np
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from utils import check_output, extra_cc_args, paddle_includes
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import paddle
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from paddle import static
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from paddle.utils.cpp_extension import get_build_directory, load
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from paddle.utils.cpp_extension.extension_utils import run_cmd
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# Because Windows don't use docker, the shared lib already exists in the
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# cache dir, it will not be compiled again unless the shared lib is removed.
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file = f'{get_build_directory()}\\multi_out_jit\\multi_out_jit.pyd'
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if os.name == 'nt' and os.path.isfile(file):
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cmd = f'del {file}'
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run_cmd(cmd, True)
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# Compile and load custom op Just-In-Time.
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multi_out_module = load(
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name='multi_out_jit',
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sources=['multi_out_test_op.cc'],
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extra_include_paths=paddle_includes, # add for Coverage CI
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extra_cxx_cflags=extra_cc_args, # test for cflags
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verbose=True,
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)
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def discrete_out_dynamic(use_custom, device, dtype, np_w, np_x, np_y, np_z):
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paddle.set_device(device)
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w = paddle.to_tensor(np_w, dtype=dtype, stop_gradient=False)
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x = paddle.to_tensor(np_x, dtype=dtype, stop_gradient=False)
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y = paddle.to_tensor(np_y, dtype=dtype, stop_gradient=False)
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z = paddle.to_tensor(np_z, dtype=dtype, stop_gradient=False)
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if use_custom:
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out = multi_out_module.discrete_out(w, x, y, z)
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else:
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out = w * 1 + x * 2 + y * 3 + z * 4
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out.backward()
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return out.numpy(), w.grad.numpy(), y.grad.numpy()
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def discrete_out_static(use_custom, device, dtype, np_w, np_x, np_y, np_z):
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paddle.enable_static()
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paddle.set_device(device)
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with (
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static.scope_guard(static.Scope()),
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static.program_guard(static.Program()),
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):
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w = static.data(name="w", shape=[None, np_x.shape[1]], dtype=dtype)
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x = static.data(name="x", shape=[None, np_x.shape[1]], dtype=dtype)
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y = static.data(name="y", shape=[None, np_y.shape[1]], dtype=dtype)
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z = static.data(name="z", shape=[None, np_z.shape[1]], dtype=dtype)
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w.stop_gradient = False
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x.stop_gradient = False
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y.stop_gradient = False
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z.stop_gradient = False
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if use_custom:
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print(static.default_main_program())
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out = multi_out_module.discrete_out(w, x, y, z)
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print(static.default_main_program())
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else:
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out = w * 1 + x * 2 + y * 3 + z * 4
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static.append_backward(out)
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print(static.default_main_program())
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exe = static.Executor()
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exe.run(static.default_startup_program())
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if paddle.framework.in_pir_mode():
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ops = static.default_main_program().global_block().ops
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if use_custom:
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fetch_list = [
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out,
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ops[-1].result(0), # w_grad
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ops[-1].result(1),
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] # y_grad
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else:
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fetch_list = [
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out,
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ops[-2].result(0), # w_grad
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ops[-3].result(0),
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] # y_grad
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else:
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fetch_list = [
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out.name,
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w.name + "@GRAD",
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y.name + "@GRAD",
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]
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out_v, w_grad_v, y_grad_v = exe.run(
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static.default_main_program(),
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feed={
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"w": np_w.astype(dtype),
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"x": np_x.astype(dtype),
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"y": np_y.astype(dtype),
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"z": np_z.astype(dtype),
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},
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fetch_list=fetch_list,
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)
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paddle.disable_static()
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return out_v, w_grad_v, y_grad_v
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class TestMultiOutputDtypes(unittest.TestCase):
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def setUp(self):
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self.custom_op = multi_out_module.multi_out
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self.dtypes = ['float32', 'float64']
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self.devices = ['cpu']
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self.np_w = np.random.uniform(-1, 1, [4, 8]).astype("float32")
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self.np_x = np.random.uniform(-1, 1, [4, 8]).astype("float32")
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self.np_y = np.random.uniform(-1, 1, [4, 8]).astype("float32")
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self.np_z = np.random.uniform(-1, 1, [4, 8]).astype("float32")
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def run_static(self, device, dtype):
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paddle.set_device(device)
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x_data = np.random.uniform(-1, 1, [4, 8]).astype(dtype)
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with (
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paddle.static.scope_guard(paddle.static.Scope()),
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paddle.static.program_guard(paddle.static.Program()),
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):
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x = paddle.static.data(name='X', shape=[None, 8], dtype=dtype)
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outs = self.custom_op(x)
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exe = paddle.static.Executor()
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exe.run(paddle.static.default_startup_program())
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res = exe.run(
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paddle.static.default_main_program(),
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feed={'X': x_data},
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fetch_list=outs,
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)
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return res
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def check_multi_outputs(self, outs, is_dynamic=False):
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out, zero_float64, one_int32 = outs
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if is_dynamic:
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zero_float64 = zero_float64.numpy()
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one_int32 = one_int32.numpy()
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# Fake_float64
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self.assertTrue('float64' in str(zero_float64.dtype))
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check_output(
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zero_float64, np.zeros([4, 8]).astype('float64'), "zero_float64"
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)
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# ZFake_int32
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self.assertTrue('int32' in str(one_int32.dtype))
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check_output(one_int32, np.ones([4, 8]).astype('int32'), "one_int32")
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def test_multi_out_static(self):
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paddle.enable_static()
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for device in self.devices:
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for dtype in self.dtypes:
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res = self.run_static(device, dtype)
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self.check_multi_outputs(res)
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paddle.disable_static()
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def test_multi_out_dynamic(self):
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for device in self.devices:
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for dtype in self.dtypes:
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paddle.set_device(device)
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x_data = np.random.uniform(-1, 1, [4, 8]).astype(dtype)
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x = paddle.to_tensor(x_data)
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outs = self.custom_op(x)
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self.assertTrue(len(outs) == 3)
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self.check_multi_outputs(outs, True)
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def test_discrete_out_static(self):
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for device in self.devices:
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for dtype in self.dtypes:
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(
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pd_out,
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pd_w_grad,
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pd_y_grad,
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) = discrete_out_static(
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False,
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device,
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dtype,
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self.np_w,
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self.np_x,
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self.np_y,
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self.np_z,
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)
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(
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custom_out,
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custom_w_grad,
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custom_y_grad,
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) = discrete_out_static(
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True,
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device,
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dtype,
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self.np_w,
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self.np_x,
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self.np_y,
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self.np_z,
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)
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check_output(custom_out, pd_out, "out")
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# NOTE: In static mode, the output gradient of custom operator has been optimized to shape=[1]. However, native paddle op's output shape = [4, 8], hence we need to fetch pd_w_grad[0][0] (By the way, something wrong with native paddle's gradient, the outputs with other indexes instead of pd_w_grad[0][0] is undefined in this unittest.)
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check_output(custom_w_grad, pd_w_grad[0][0], "w_grad")
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check_output(custom_y_grad, pd_y_grad[0][0], "y_grad")
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def test_discrete_out_dynamic(self):
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for device in self.devices:
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for dtype in self.dtypes:
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(
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pd_out,
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pd_w_grad,
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pd_y_grad,
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) = discrete_out_dynamic(
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False,
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device,
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dtype,
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self.np_w,
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self.np_x,
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self.np_y,
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self.np_z,
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)
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(
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custom_out,
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custom_w_grad,
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custom_y_grad,
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) = discrete_out_dynamic(
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True,
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device,
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dtype,
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self.np_w,
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self.np_x,
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self.np_y,
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self.np_z,
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)
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check_output(custom_out, pd_out, "out")
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check_output(custom_w_grad, pd_w_grad, "w_grad")
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check_output(custom_y_grad, pd_y_grad, "y_grad")
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if __name__ == '__main__':
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unittest.main()
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